US12002549B2ActiveUtilityA1
Knowledge reuse-based method and system for predicting cell concentration in fermentation process
Est. expiryJul 26, 2042(~16 yrs left)· nominal 20-yr term from priority
G16B 5/30C12M 41/48G16B 40/20G16C 20/30G06N 5/00Y02P90/30
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Claims
Abstract
The present invention provides a knowledge reuse-based method and system for predicting a cell concentration in a fermentation process. The method includes: constructing a cell concentration soft sensor universal model in a fermentation process; acquiring and preprocessing process data of a fermentation stage A; determining a cell concentration soft sensor model of the fermentation stage A; designing a cell concentration online soft sensor of a fermentation stage B; and predicting a cell concentration of the fermentation stage B according to the cell concentration online soft sensor of the fermentation stage B. The present invention resolves the problems of weak generalization of a cell concentration soft sensor model and high costs of establishing models for fermentation stages separately, thereby improving the prediction accuracy of a cell concentration soft sensor.
Claims
exact text as granted — not AI-modifiedWhat is claimed is:
1. A knowledge reuse-based method for predicting a cell concentration in a fermentation process, comprising:
S 1 : constructing a cell concentration soft sensor universal model in a fermentation process, wherein the fermentation process is divided into four stages in time order: a lag phase, an exponential growth phase, a stationary phase, and a decline phase, and for two stages that occur successively, it is defined that a former stage is a fermentation stage A and a latter stage is a fermentation stage B;
S 2 : acquiring and preprocessing process data of the fermentation stage A;
S 3 : determining a cell concentration soft sensor model of the fermentation stage A based on the cell concentration soft sensor universal model in combination with a process data result of the fermentation stage A after the preprocessing;
S 4 : acquiring process data of the fermentation stage B, and after preprocessing, designing a cell concentration online soft sensor of the fermentation stage B with the cell concentration soft sensor model of the fermentation stage A; and
S 5 : predicting a cell concentration of the fermentation stage B according to the cell concentration online soft sensor of the fermentation stage B,
wherein a method for designing a cell concentration online soft sensor of the fermentation stage B in step S 4 is:
S 41 : setting a parameter estimation of the cell concentration soft sensor model of the fermentation stage B to:
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wherein at a moment k+τ+1, for the cell concentration soft sensor model of the fermentation stage B, a delay is τ B =τ A , orders are p B =p A and q B =q A , the parameter estimation is {circumflex over (θ)} k+τ+1 =[â, ⋅ ⋅ ⋅ , â p B , {circumflex over (b)} 1 , ⋅ ⋅ ⋅ , {circumflex over (b)} q B ] T , â 1 , ⋅ ⋅ ⋅ , â p B , {circumflex over (b)} 1 , ⋅ ⋅ ⋅ , {circumflex over (b)} q B is an estimate of each parameter in the parameter vector of the fermentation stage B at the moment k+τ+1, where a is a parameter of a autoregressive model and b is a parameter of a moving model, {circumflex over (θ)} A is a parameter estimation of the fermentation stage A, Ĥ k+τ+1 is a gain matrix of the cell concentration soft sensor model of the fermentation stage B at the moment k+τ+1, E k+τ+1 is an innovation vector at the moment k+τ+1, Y k+τ+1 is a cell concentration matrix of the fermentation stage B at the moment k+τ+1, and X k+τ+ 1 is an input matrix of the fermentation stage B at the moment k+τ+1;
S 42 : calculating the gain matrix Ĥ k+τ+1 of the cell concentration soft sensor model of the fermentation stage B in step S 41 ;
S 43 : designing the cell concentration online soft sensor of the fermentation stage B based on a parameter estimation vector {circumflex over (θ)} k+τ+1 and the gain matrix Ĥ k+τ+1 of the cell concentration soft sensor model of the fermentation stage B.
2. The knowledge reuse-based method for predicting a cell concentration in a fermentation process according to claim 1 , wherein a method for constructing a cell concentration soft sensor universal model in a fermentation process in step S 1 comprises:
S 11 : selecting a dilution ratio as an auxiliary variable based on dynamic characteristics of the fermentation process, and setting the cell concentration soft sensor model to:
y k+τ +a 1 y k+τ−1 + ⋅ ⋅ ⋅ +a p y k+τ−p =b 0 u k +b 1 u k−1 + ⋅ ⋅ ⋅ +b q u k−q +v k+τ ,
wherein k is a moment, τ is the delay of the soft sensor model, p and q are the orders of the soft sensor model, a and b are coefficients, y k+τ is a cell concentration at a moment k+τ, u k is an auxiliary variable at the moment k, v k+τ is a cell concentration measurement noise at the moment k+τ, and a type of the noise is selected from white noises satisfying Gaussian distribution, t distribution, and Poisson distribution; and
S 12 : performing vector transformation on the cell concentration soft sensor model, to obtain the cell concentration soft sensor universal model:
y k+τ =x k+τ T θ+v k+τ ,
wherein an input vector is x k+τ =[y k+τ−1 y k+τ−2 ⋅ ⋅ ⋅ y k+τ−p u k ⋅ ⋅ ⋅ u k−q ] T , and a parameter is θ=[a 1 , ⋅ ⋅ ⋅ , a p , b 0 , ⋅ ⋅ ⋅ , b q ] T .
3. The knowledge reuse-based method for predicting a cell concentration in a fermentation process according to claim 1 , wherein a method for preprocessing process data of the fermentation stage A in step S 2 is:
eliminating a nonnumerical sample point in the process data of the fermentation stage A, and eliminating abnormal working condition data according to a working condition record; eliminating an outlier in the process data of the fermentation stage A; filling a missing value in the process data of the fermentation stage A; and removing a dimensional difference between an auxiliary variable and a quality variable in the fermentation stage A.
4. The knowledge reuse-based method for predicting a cell concentration in a fermentation process according to claim 1 , wherein a method for calculating the gain matrix Ĥ k+τ+1 of the cell concentration soft sensor model of the fermentation stage B in step S 42 comprises:
step 1: defining a loss function of a knowledge reuse-based soft sensor model:
J =trace{ E [(θ B −{circumflex over (θ)} k+τ+1 )(θ B −{circumflex over (θ)} k+τ+1 ) T ]},
wherein θ B is an actual parameter value of the fermentation stage B, {circumflex over (θ)} k+τ+1 is the parameter estimation of the fermentation stage B at the moment k+τ+1, E[⋅] is an averaging operation, trace {⋅} is a trace operation of a matrix, J is a loss function with respect to Ĥ k+τ+1 ; and
step 2: calculating the gain matrix Ĥ k+τ+1 based on a method of minimizing the loss function:
Ĥ k+τ+1 =( F k+τ+1 +{circumflex over (D)} k+τ+1 −1 ) −1 X k+1 T Σ k+1 −1 ,
wherein F k+τ+1 =X k+1 T Σ k+τ+1 −1 X k+1 , {circumflex over (D)} k+τ+1 −1 ={circumflex over (d)} k+τ+1 {circumflex over (d)} k+τ+1 T ,
X k+1 T {circumflex over (d)} k+τ+1 =E k+τ+1 ,
F k+τ+1 is a Fisher information matrix of the soft sensor model of the fermentation stage B at the moment k+τ+1, Σ k+1 −1 is an inverse of a measurement noise covariance matrix of the fermentation stage B at a moment k+1, {circumflex over (D)} k+τ+1 − is a difference covariance matrix between the fermentation stage A and the fermentation stage B at the moment k+τ+1, and {circumflex over (d)} k+τ+1 is a parameter difference between the fermentation stage A and the fermentation stage B at the moment k+τ+1.
5. The knowledge reuse-based method for predicting a cell concentration in a fermentation process according to claim 1 , wherein a method for designing the cell concentration online soft sensor of the fermentation stage B based on a parameter estimation vector {circumflex over (θ)} k+τ+1 and the gain matrix Ĥ k+τ+1 of the cell concentration soft sensor model of the fermentation stage B in step S 43 comprises:
step 1: initializing {circumflex over (d)} 0 , G 0 , and Q 0 at an initial moment of the fermentation stage B;
wherein {circumflex over (d)} is a model parameter difference between the fermentation stage A and the fermentation stage B, {circumflex over (d)} 0 and G 0 are (p B +q B )-dimensional zero vectors, and Q 0 is a (p B +q B )×(p B +q B )-dimensional zero matrix;
step 2: solving the cell concentration online soft sensor of the fermentation stage B, specifically denoted as follows:
{circumflex over (θ)} k+τ+1 ={circumflex over (θ)} A +P k+τ+1 G k+τ+1 ,
where
F k+τ+1 =F k+τ +σ k+1 −2 x k+τ+1 x k+τ+1 T =Q k+τ +f k+τ+1 ,
G k+τ+1 =G k+τ +σ k+τ+1 −2 x k+τ+1 ( y k+τ+1 −x k+τ+1 T {circumflex over (θ)} A )= G k+τ +g k+τ+1 ,
P k+τ+1 ≤( F k+τ+1 +{circumflex over (D)} k+τ+1 −1 ) −1 ,
{circumflex over (θ)} A is the parameter estimation of the fermentation stage A, {circumflex over (θ)} k+τ+1 is the parameter estimation of the fermentation stage B at the moment k+τ+1, σ k+τ+1 −2 is a measurement noise variance of a cell concentration of the fermentation stage B at the moment k+τ+1, x k+τ+1 is an input vector of the fermentation stage B at the moment k+τ+1, y k+τ+1 is a cell concentration of the fermentation stage B at the moment k+τ+1, and when new measurement data is acquired, F k+τ+1 has updated data quality of the fermentation stage B, and G k+τ+ 1 and P k+τ+1 have updated a difference between the fermentation stages A and B; and
step 3: before the fermentation stage B ends, when new measurement data is acquired, sequentially calculating F k+τ+1 , G k+τ+1 , and P k+τ+1 , and updating a parameter {circumflex over (θ)} k+τ+1 of the soft sensor model.
6. The knowledge reuse-based method for predicting a cell concentration in a fermentation process according to claim 2 , wherein a method for predicting a cell concentration of the fermentation stage B according to the cell concentration online soft sensor of the fermentation stage B in step S 5 comprises:
introducing the parameter estimation {circumflex over (θ)} k+τ+1 of the soft sensor into the soft sensor universal model y k+τ =x k+τ T θ+v k+τ , to obtain a predicted cell concentration value ŷ k+τ+1 of the fermentation stage B:
ŷ k+τ1 =x k+τ+1 T {circumflex over (θ)} k+τ+1 ,
wherein θ is a parameter of the universal model, {circumflex over (θ)} k+τ+1 is the parameter estimation of the fermentation stage B at the moment k+τ+1, x k+τ is the input vector at the moment k+τ, v k+τ is the cell concentration measurement noise at the moment k+τ, ŷ k+τ+1 is the predicted cell concentration value of the fermentation stage B at the moment k+τ+1.
7. The knowledge reuse-based method for predicting a cell concentration in a fermentation process according to claim 4 , wherein the method of minimizing the loss function is selected from a feasible direction method, a quadratic programming method, a particle swarm algorithm, Bayesian optimization, and a random search and gradient descent method.
8. The knowledge reuse-based method for predicting a cell concentration in a fermentation process according to claim 4 , wherein a method for calculating the inverse of the noise covariance matrix is selected from a Kalman filter and an extended form thereof, statistical hypothesis testing, and regression analysis.
9. The knowledge reuse-based method for predicting a cell concentration in a fermentation process according to claim 4 , wherein a method for calculating the model parameter difference {circumflex over (d)} between the fermentation stage A and the fermentation stage B is selected from a recursive least squares method, a recursive extended least squares method, a recursive maximum likelihood method, a random Newton method, Kalman estimation, a prediction error method, and a long short-term memory network.Cited by (0)
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